Objectifs.md

@@ -7,13 +7,9 @@ François Laurent, Bertrand Néron, Vincent Guillemot, Étienne Kornobis
 ## Objectifs 
 
 - Savoir importer des données dans un environnement Python 
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 - Savoir appliquer des méthodes univariées : t-test, régression linéaire, ANOVA, test du Chi-deux d’indépendance ou de comparaison des proportions, tests non paramétriques 
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 - Savoir appliquer des méthodes multivariées : ACP, classification ascendante hiérarchique, distances 
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 - Savoir visualiser des données et des résultats d’analyse 
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 - Savoir réaliser un rapport d’analyse statistique avec Jupyter 
 
 ## Objectifs supplémentaires 
@@ -27,7 +23,6 @@ Connaître les concepts clefs du Machine Learning : apprentissage, test, biais
 - Comprendre la notion de classe et d’objet en Python, et savoir utiliser des objets 
 - Avoir des connaissances de base en statistique 
 - Être familier avec les environnements virtuels 
-
  
 # Course Objectives for "Scientific Python" 
 
@@ -37,30 +32,20 @@ François Laurent, Bertrand Néron, Vincent Guillemot, Étienne Kornobis
 
 ## Objectives: 
 
-    Know how to import data into a Python environment. 
-
-    Understand and apply univariate methods: t-test, linear regression, ANOVA, Chi-square test of independence or comparison of proportions, non-parametric tests. 
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-    Understand and apply multivariate methods: PCA (Principal Component Analysis), hierarchical clustering, distances. 
-
-    Know how to visualize data and analysis results. 
-
-    Know how to create a statistical analysis report using Jupyter. 
+- Know how to import data into a Python environment. 
+- Understand and apply univariate methods: t-test, linear regression, ANOVA, Chi-square test of independence or comparison of proportions, non-parametric tests. 
+- Understand and apply multivariate methods: PCA (Principal Component Analysis), hierarchical clustering, distances. 
+- Know how to visualize data and analysis results. 
+- Know how to create a statistical analysis report using Jupyter. 
 
 ## Additional Objectives: 
 
-    Understand key concepts of Machine Learning: learning, testing, bias, classification, cross-validation, scoring. Know how to build a Machine Learning pipeline with scikit-learn. 
+- Understand key concepts of Machine Learning: learning, testing, bias, classification, cross-validation, scoring. Know how to build a Machine Learning pipeline with scikit-learn. 
 
 ## Prerequisites: 
 
-    Proficiency in manipulating file paths. 
-
-    Ability to create loops and nested loops. 
-
-    Understanding the concept of class and object in Python, and ability to use objects. 
-
-    Basic knowledge of statistics. 
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-    Familiarity with virtual environments. 
-
- 
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+- Proficiency in manipulating file paths. 
+- Ability to create loops and nested loops. 
+- Understanding the concept of class and object in Python, and ability to use objects. 
+- Basic knowledge of statistics. 
+- Familiarity with virtual environments.